Uplink Data Transmission Research Articles

Protocol for personal RF-EMF exposure measurement studies in 5th generation telecommunication networks

BackgroundThe general population is exposed to Radio-Frequency Electromagnetic Fields (RF-EMFs) used by telecommunication networks. Previous studies developed methods to assess this exposure. These methods will be inadequate to accurately assess exposure in 5G technologies or other wireless technologies using adaptive antennas. This is due to the fact that 5G NR (new radio) base stations will focus actively on connected users, resulting in a high spatio-temporal variations in the RF-EMFs. This increases the measurement uncertainty in personal measurements of RF-EMF exposure. Furthermore, a user’s exposure from base stations will be dependent on the amount of data usage, adding a new component to the auto-induced exposure, which is often omitted in current studies.GoalsThe objective of this paper is to develop a general study protocol for future personal RF-EMF exposure research adapted to 5G technologies. This protocol will include the assessment of auto-induced exposure of both a user’s own devices and the networks’ base stations.MethodThis study draws from lessons learned from previous RF-EMF exposure research and current knowledge on 5G technologies, including studies simulating 5G NR base stations and measurements around 5G NR test sites.ResultsTo account for auto-induced exposure, an activity-based approach is introduced. In survey studies, an RF-EMF sensor is fixed on the participants’ mobile device(s). Based on the measured power density, GPS data and movement and proximity sensors, different activities can be clustered and the exposure during each activity is evaluated. In microenvironmental measurements, a trained researcher performs measurements in predefined microenvironments with a mobile device equipped with the RF-EMF sensor. The mobile device is programmed to repeat a sequence of data transmission scenarios (different amounts of uplink and downlink data transmissions). Based on simulations, the amount of exposure induced in the body when the user device is at a certain location relative to the body, can be evaluated.ConclusionOur protocol addresses the main challenges to personal exposure measurement introduced by 5G NR. A systematic method to evaluate a user’s auto-induced exposure is introduced.

Wavelength Tunable Asymmetric B-OWC System Based on Self-Injection Locking for TDM-PONs

The challenge of asymmetric bidirectional optical wireless communication is majority resulted from the power shortage of uplink transmission, since it is double the downlink transmission distance. In this research, a wavelength tunable self-injection locking (SIL) system based on free-space configuration for uplink transmission in a modulated retroreflector based asymmetric bidirectional optical wireless communication system is proposed and experimentally evaluated. The proposed works were investigated from access node (AN) to terminal user(TU) respectively. A power gain of 34.51 dB, and uplink data transmission test over 2 Gbits/s, with Q-factors beyond FEC limit, are reported.

Energy-Efficient Resource Allocation in Massive MIMO-NOMA Networks With Wireless Power Transfer: A Distributed ADMM Approach

In multicell massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks, base stations (BSs) with multiple antennas deliver their radio frequency energy in the downlink, and Internet-of-Things (IoT) devices use their harvested energy to support uplink data transmission. This paper investigates the energy efficiency (EE) problem for multicell massive MIMO NOMA networks with wireless power transfer (WPT). To maximize the EE of the network, we propose a novel joint power, time, antenna selection, and subcarrier resource allocation scheme, which can properly allocate the time for energy harvesting and data transmission. Both perfect and imperfect channel state information (CSI) are considered, and their corresponding EE performance is analyzed. Under quality-of-service (QoS) requirements, an EE maximization problem is formulated, which is non-trivial due to non-convexity. We first adopt nonlinear fraction programming methods to convert the problem to be convex, and then, develop a distributed alternating direction method of multipliers (ADMM)- based approach to solve the problem. Simulation results demonstrate that compared to alternative methods, the proposed algorithm can converge quickly within fewer iterations, and can achieve better EE performance.

Joint time‐slot and power allocation algorithm for data and energy integrated networks supporting internet of things (IoT)

SummaryIoT is an essential enabler for smart cities and smart society. However, its deployment at large scale faces a big challenge: battery replacement as most IoT devices are battery‐powered or even battery‐less. In a hostile environment, it is infeasible to replace batteries. Radio frequency (RF)‐enable wireless energy transfer (WET) is a promising technology to solve this problem. Since RF is also used for wireless data communication, a data and energy integrated network (DEIN) is the way forward. Based on the DEIN technology, a time allocation model is designed in this paper to manage the RF energy and uplink data transmission in different time slots. In the IoT scenario, the DEIN's primary service is to collect environmental information such as temperature, humidity, and luminance. Therefore, the uplink data transmission of the battery‐powered/battery‐less IoT nodes deserves more attention. To increase the uplink data transmission in case of consuming less energy in the DEIN system, we propose a joint time slot and power allocation algorithm to minimize the system's consumed energy for transmitting per bit of uplink data. It aims to maximize the efficiency of the DEIN system's energy utilization, which helps to achieve an energy‐efficient DEIN.

A Fully Distributed and Clustered Learning of Power Control in User-Centric Ultra-Dense HetNets

In this paper, we investigate a power control of uplink connection in the user-centric ultra-dense heterogeneous networks (HetNets), which are studied as different types of access points (APs). The main objective of this investigation is to engage users in a cooperative game to confront with the problem of per-user power control and coordinate multi-user interferences. Thus, we formulate the optimization problem as a sum of the users’ cooperative energy efficiency (EE) functions. Firstly, a realistic and new model of the user device's power consumption is proposed. This model includes the power used for operating modes and signal processing of mobile devices during the uplink data transmission. Secondly, the EE optimization problem is formulated by maximizing the sum of the users’ cooperative EE function subject to each user's quality-of-service (QoS) and a power constraint. Then, we propose a fully distributed and clustered learning scheme for solving the optimization problem, where neighboring users are clustered to engage in the cooperative game of power control in order to coordinate the multi-user interferences. It is theoretically proved that the size of the clusters has an impact on the sum of the users’ cooperative EE. Additionally, our scheme can achieve convergence with imperfect channel feedback and the local knowledge of the user's constraints. Finally, simulation results confirm the theoretical analysis and demonstrate the robust performance of the proposed scheme compared with two benchmark methods.

Connectivity Performance Evaluation for Grant-Free Narrowband IoT With Widely Linear Receivers

Future wireless cellular communication networks are expected to provide connectivity for massive machine-type communication (mMTC) devices. The main challenge of supporting mMTC traffic for a cellular network lies in the high density of these devices, which individually have relatively little data to transmit. This suggests the use of low overhead, grant-free access scheme for uplink data transmission, which, however, suffers from packet collisions when devices attempt to access the channel. In this article, we suggest the use of real-valued transmission together with widely linear (WL) reception for improving resource access and thus data throughput in the uplink of mMTC traffic scenarios. We show that not surprisingly, the WL scheme can virtually double the number of receive antennas at the base station (BS). We analyze the effect of this on the supported user density and data throughput for grant-free uplink transmission. As a specific example, we consider the narrowband Internet-of-Things (NB-IoT) cellular communication system, which already includes real-valued modulation modes. Our numerical results show that the supported user density and data throughput of grant-free NB-IoT systems can be significantly improved due to the use of WL receivers. For example, considering an NB-IoT system with 1% packet drop probability, we obtain a tenfold (for single-antenna BS) and a sixfold (for dual-antenna BS) increase in the supported user density by using WL receivers instead of their conventional linear counterparts.

Statistical-QoS Guarantee for IoT Network Driven by Laser-Powered UAV Relay and RF Backscatter Communications

Resource optimization is investigated for an unmanned aerial vehicle (UAV)-mounted relay assisted Internet-of-Things (U-IoT) network. A comprehensive network structure is proposed by incorporating laser-driven adaptive wireless-power-transfer at the UAV relay, wirelessly powered backscatter communication in the radio-frequency access links, and modulating retro-reflector based free space optical backhaul link in an optimization framework. Our objective is to maximize the number of the connected IoT devices with the UAV relay for uplink data transmission while satisfying the heterogeneous quality-of-service requirements of the IoT devices. Towards this objective, a novel optimization problem is formulated by considering queueing-overflow probability constraints of the IoT devices with stochastic data arrival, backhaul capacity constraint, and energy causality constraint at the UAV relay. The considered resource optimization is NP-hard, and an iterative solution is proposed by exploiting structure of the optimization problem. Furthermore, a three-stage optimization is devised to solve an NP-complete fractional optimization problem at each iteration of the proposed solution. An algorithm of polynomial computational complexity is developed for joint connectivity maximization and resource allocation, and convergence of the developed algorithm is proved. Using extensive simulations, efficiency of the proposed algorithm is demonstrated for improving the supportable arrival rate per IoT device and the number of the connected IoT devices in uplink of a U-IoT network.

Joint Resource Allocation and UAV Trajectory Optimization for Space–Air–Ground Internet of Remote Things Networks

Given the limited transmission power of smart devices in Internet of Remote Things (IoRT), unmanned aerial vehicle (UAV) aided space-air-ground (SAG) networks become a beneficial remedy for uplink data transmission in IoRT networks. In this article, we propose a SAG–IoRT framework, where drones act as relays to upload the data from smart devices to low earth orbit satellites. Considering the large number of smart devices, we maximize the system capacity by jointly optimizing smart devices connection scheduling, power control, and UAV trajectory, where the joint optimization is a nonconvex optimization problem. The formulated problem is a mixed integer nonconvex optimization problem, which is challenging to solve directly. Hence, an efficient iterative algorithm is proposed for solving the above-mentioned nonconvex optimization problem by applying variable substitution, successive convex optimization techniques, and the block coordinate decent algorithm. In particular, we alternately iterate smart device connection scheduling, power control, and UAV trajectory design to obtain the maximum system capacity. Numerical simulation results show that our proposed algorithm substantially improves the system capacity in comparison to the static UAV scheme, and achieves a gain of at least 22.3% in terms of the capacity against dynamic UAV scheme with a circular trajectory.

Design, Analysis, and Optimization of a Large Intelligent Reflecting Surface-Aided B5G Cellular Internet of Things

In this article, we apply the large intelligent reflecting surface (IRS) technique in beyond fifth-generation (B5G) cellular Internet of Things (IoT) to satisfy the requirements of massive connectivity, low power, and wide coverage. First, we design a framework for the large IRS-aided B5G cellular IoT, including channel estimation, uplink data transmission, and downlink data transmission. Then, we analyze the performance of the proposed framework, and reveal the impacts of key parameters of the large IRS on the spectral efficiency. Next, we propose a low-complexity time-length allocation algorithm to minimize the total energy consumption of B5G cellular IoT. Finally, extensive simulation results validate the accuracy of the derived theoretical expressions and the effectiveness of the proposed algorithm.

Sum‐rate maximization in uplink cell‐free massive multiinput multioutput system with jamming

AbstractThis article takes the first look at jamming attacks on both uplink training and data transmission phases in uplink cell‐free massive multi‐input multi‐output systems. Closed‐form rate expressions for that system are derived, which provide us with the opportunities to analyze the achievable rate in the presence of jamming. To compensate for the sum‐rate degradation caused by jamming, we propose an iterative sum‐rate maximization power control algorithm without redesigning the estimator and receiver. Specifically, by virtue of the Lagrange multiplier methodology, the proposed algorithm can determine the closed‐form solution to each iteration and thus, benefiting from less runtime. Simulation results reveal the effectiveness of the proposed algorithm.

Asymmetrical bidirectional optical wireless communication system based on a transmissive 1D LC-SLM for NG-PON2.

An asymmetric bidirectional optical wireless communication (B-OWC) system using optical fibers as the transmitter and receiver and capable of wavelength switching is proposed for next generation passive optical network stage two (NG-PON2) extension to home area network (HAN), without optical/electrical (O/E) and electrical/optical (E/O) conversions, which is based on a transmissive "floating" pixel size one-dimensional (1D) liquid crystal spatial light modulator (LC-SLM) at the access node (AN) and a modulated retroreflector (MRR) at the terminal user (TU). The utilization of a reflective grating element and a low-cost LC-SLM in the AN offers a high flexibility of beam steering and wavelength selection in the scenario of B-OWC. Instead of a light source used at conventional TU, a MRR was employed at a TU in order to improve the problems of the power consumptions, alignment difficulties, wavelength contentions, and cost of the TUs. Downlink and uplink data transmissions at a link speed over 2.5 Gbits/s, using wavelengths between 1590∼1603nm and 1524∼1544nm, respectively, corresponding to the downlink and uplink wavelengths specified by the NG-PON2 standard, have been experimentally demonstrated successfully, with quality factors (Q-factors) beyond the forward error correction limit (Q-factor=16.9dB, bit error rate=10-12).

Long-Term Scheduling and Power Control for Wirelessly Powered Cell-Free IoT

We investigate the long-term scheduling and power control scheme for a wirelessly powered cell-free Internet-of-Things (IoT) network which consists of distributed access points (APs) and a large number of sensors. In each time slot, a subset of sensors is scheduled for uplink data transmission or downlink power transfer. Through asymptotic analysis, we obtain closed-form expressions for the harvested energy and the achievable rates that are independent of random pilots. Then, using these expressions, we formulate a long-term scheduling and power control problem to maximize the minimum time-average achievable rate among all sensors while maintaining the battery state of each sensor higher than a predefined minimum level. Using Lyapunov optimization, the transmission mode, the active sensor set, and the power control coefficients for each time slot are jointly determined. Finally, simulation results validate the accuracy of our derived closed-form expressions and reveal that the minimum time-average achievable rate is boosted significantly by the proposed scheme compared with the simple greedy transmission scheme.

Energy Efficient Transmit-Receive Hybrid Spatial Modulation for Large-Scale MIMO Systems

We consider a point to point large-scale multiple-input multiple-output (MIMO) system operating in the millimeter wave (mmWave) band and an outdoor scenario. Novel transmit and receive spatial modulation (SM) schemes are proposed for uplink (UL) and downlink (DL) data transmission phases based on a novel energy efficient hybrid user terminal architecture. The analog circuitry of the proposed hybrid architecture is divided into two stages: phase shifters and analog switches. The phase shifting stage assures high gain and overcomes the severe path-loss caused by outdoor mmWave propagation. The analog switching stage smartly allocates the antennas to be used at the phase shifting stage and combats the spatial correlation. We provide the analysis of the spectral efficiency $({{SE}})$ of the UL and DL systems. Next, we propose a reduced complexity algorithm that jointly optimizes the analog beamformer and combiner design of the UL and DL circuitry to maximize the energy efficiency $({{EE}})$ . Finally, we compare and evaluate the performance of the proposed algorithm in terms of the ${{SE}}$ and ${{EE}}$ assuming both stochastic and realistic channel models.

Simultaneous wireless information and power transfer for massive MIMO networks

In this study, the authors propose a simultaneous wireless information and power transfer scheme based on the power-splitting (PS) protocol for a massive multiple-input multiple-output (MIMO) network. On the downlink, the base station transmits the information and energy simultaneously to each terminal and each terminal divides the received signals by its PS device between information decoder and energy harvester (EH). On the uplink, a portion of the energy harvested by EH is drawn for uplink pilot transmission in the coming frame and then the other is used for uplink data transmission in the current frame. Based on this scheme, the closed-form lower bound expressions are obtained for the achievable downlink rate and uplink rate of each terminal and then are utilised to formulate an optimisation problem that is to maximise achievable sum rate of the whole network while ensuring the minimum required uplink and downlink rates of each terminal. However, as the original optimisation problem is non-convex and non-linear and very hard to solve directly, an algorithm is proposed by utilising successive approximation method based on geometric program. Numerical results verify the correctness and feasibility of the proposed algorithm.

Wireless Powered Wearables Using Distributed Massive MIMO

This paper presents an analytical framework which lays the foundation for distributed massive multiple-input multiple-output (MIMO) supported wireless power transfer for wearable devices. In our approach, we consider multiple users, each wearing a number of wireless sensors along with one body worn hub which acts as a relay to forward information from the on-body sensors to nearby access points (APs). Each AP is equipped with a large number of antennas and is not only responsible for receiving data sent from the hubs, but also supplying wireless power to them. Interaction between hubs and APs is not exclusive to a single pairing, with APs assigned to supply energy to and receive data from more than one hub. More precisely, APs perform the downlink energy transmission and the uplink data transmission using maximum-ratio combining. Analytical approximations of the outage probability and spectral efficiency are derived. Based on these analytical results, two modes of operation are investigated. These are outage probability prioritized and spectral efficiency prioritized. For each mode, max-min power controls are proposed to ensure a uniformly good service throughout the area of coverage. By contrasting with the IEEE 802.15.6 standard, the numerical results illustrate that while using collocated massive MIMO may provide unsatisfactory performance, the distributed setting shows much promise for enabling wireless power transfer for wearable devices.

An Efficient Polar Coding Scheme for Uplink Data Transmission in Narrowband Internet of Things Systems

Narrowband Internet of Things (NB-IoT) is an emerging IoT cellular-based wireless technology that builds based on the existing LTE standard to extend its network coverage suited for Low Power Wide Area Network (LPWAN) connectivity. In the NB-IoT system, LTE turbo codes and Cyclic Redundancy Check (CRC) codes are adopted as the main channel coding technique for Narrowband Physical Uplink Shared Channel (NPUSCH). However, turbo codes require iterative decoding algorithm at the expense of high computation complexity of decoding hence is crucial to achieving the LPWAN requirement of NB-IoT systems. Polar codes are best suited in this regard due to its capacity approaching and complexity reduction in short block length code. In this paper, we proposed an efficient polar coding technique using the Belief Propagation (BP) decoding algorithm for uplink data transmission on the NPUSCH channel. Furthermore, the BP algorithm is incorporated with the CRC stoppage criterion to decrease the number of decoding iteration and reduce computational complexity. In this scheme, single-tone numerology of NPUSCH using 3.75 kHz and 15 kHz subcarrier spacing is adopted. Then, the encoded data is generated with different NPUSCH resources. The theoretical formulation and simulation demonstrate that the proposed scheme provides better error rate performance over the adopted LTE turbo codes and other polar decoding algorithms while reducing the computational complexity.

Sparse detection with orthogonal matching pursuit in multiuser uplink quadrature spatial modulation MIMO system

Quadrature spatial modulation (QSM) is one of the most prevalent transmission techniques for future wireless mobile network due to its high spectral efficiency (SE) and low complexity. However, it is restrictive to downlink of the cellular network deployments. Therefore, this paper proposes QSM for the multiuser uplink data transmission that increases the SE of the network by deploying multiple antennas at the mobile user with only two radio frequency (RF) chains. Maximum likelihood (ML) decoder is used to detect the received signal and attains the optimal detection performance but it is impractical because of its high computational complexity. Consequently, this paper proposes compressed sensing (CS) based orthogonal matching pursuit (OMP) detection as it has a suboptimal performance with a low computational complexity which can be a practical solution in the high-density uplink multiuser network. However, conventional OMP algorithm has a low estimation performance due to multiuser interference which corrupt channel matrix. Thus, this work design an equalizer that mitigates the multiuser interference and improve the detection performance by orthonormalizing the columns of channel matrix. Simulation analysis confirm that the proposed QSM technique based on CS detector outperforms the conventional schemes in terms of SE and bit error rate (BER).

Low-Cost Design of Massive Access for Cellular Internet of Things

In this paper, we investigate the issue of low-cost design of massive access for cellular internet of things (IoT) over spatially correlated Rician fading channels. Specifically, by exploiting a low-overhead transmission protocol, a base station (BS) equipped with a large-scale antenna array and low-resolution analog-to-digital converters (ADCs) is deployed to serve a massive number of IoT devices with low-complexity successive interference cancellation (SIC) receivers. We first analyze the impacts of the low-cost design on the system performance and derive closed-form expressions for uplink and downlink spectral efficiencies of the cellular IoT. Then, for alleviating the negative impacts of the low-cost design, we propose an algorithm allocating the time for channel estimation, uplink data transmission, and downlink data transmission in a data frame. Finally, extensive simulation results confirm the effectiveness of the proposed low-cost design for the cellular IoT.

A Sub-mm3 Ultrasonic Free-Floating Implant for Multi-Mote Neural Recording

A 0.8-mm3-wireless, ultrasonically powered, free-floating neural recording implant is presented. The device is comprised only of a 0.25-mm2 recording integrated circuit (IC) and a single piezoceramic resonator that are used for both power harvesting and data transmission. Uplink data transmission is performed by the analog amplitude modulation of the ultrasound echo. Using a 1.78-MHz main carrier, >35 kb/s/mote equivalent uplink data rate is achieved. A technique to linearize the echo amplitude modulation is introduced, resulting in $\mu \text{W}$ , while the neural recording front end consumes $4~\mu \text{W}$ and achieves a noise floor of 5.3 $\mu V_{\text {rms}}$ in a 5-kHz bandwidth. This work improves the sub-mm recording mote depth by >2.5 $\times $ , resulting in the highest measured depth/volume ratio by $\sim 3\times $ . Orthogonal subcarrier modulation enables simultaneous operation of multiple implants, using a single-element ultrasound external transducer. Dual-mote simultaneous power-up and data transmission are demonstrated at a rate of 7 kS/s at the depth of 50 mm.

Enhancement of Opportunistic Co-Occurrence of U-LTE and Wi-Fi/Iot in 5 GHz Using Cognitive Radio

In this modern era, Unlicensed Long-Term Evolution (U-LTE) is the most interested research area to enable its access in 5GHz ISM unlicensed band for increasing data rate and spectral efficiency. The major constraint for accessing these frequencies is being able to coexist with other Wi-Fi/IoT users. Such constraint has been tackled by developing Wi-Fi/IoT by using Cognitive Radio Network (CRN) with two objectives such as deploying the Listen-Before-Talk (LBT) regulatory requirement of radio communication in U-LTE and enhancing their co-existence with Wi-Fi/IoT users in a non-interference manner. However, uplink data transmission was not considered in the unlicensed spectrum usage and also the spectrum efficiency was less. Hence, in this paper, the co-occurrence between LTE and Wi-Fi/IoT using CRN is further enhanced and realized. Initially, the co-occurrence between LTE and Wi-Fi in the unlicensed spectrum is enhanced by proposing efficient spectrum utilization based on Conflict-Tolerant Channel Allocation (CTCA) algorithm that reduces the channel inconsistencies efficiently. Moreover, this algorithm is enhanced to increase the spectrum efficiency and simultaneous transmissions in the similar channel based on an enhanced Cell ON/OFF mechanism which optimizes the resource allocation. Finally, the simulation results show that the performance efficiency of the proposed system compared to the existing in terms of spectrum efficiency, throughput and transmission time delay.